System and method for ranking content popularity in a content-centric network

ABSTRACT

One embodiment of the present invention provides a system for ranking content popularity in a content-centric network (CCN) content cache. During operation, the system receives an interest in a piece of content stored in the content cache, services the interest by accessing the piece of content, updates a service rate associated with the piece of content, updates system-wide service rate statistics, and determines a popularity level associated with the piece of content based on the updated service rate and the updated system-wide service rate statistics.

RELATED APPLICATIONS

The subject matter of this application is related to the subject matter in the following applications:

-   -   U.S. patent application Ser. No. 14/065,691 (Attorney Docket No.         PARC-20130997US01), entitled “SYSTEM AND METHOD FOR HASH-BASED         FORWARDING OF PACKETS WITH HIERARCHICALLY STRUCTURED         VARIABLE-LENGTH IDENTIFIERS,” by inventors Marc E. Mosko and         Michael F. Plass, filed 29 Oct. 2013;     -   U.S. patent application Ser. No. 14/067,857 (Attorney Docket No.         PARC-20130874US01), entitled “SYSTEM AND METHOD FOR MINIMUM PATH         MTU DISCOVERY IN CONTENT CENTRIC NETWORKS,” by inventor Marc E.         Mosko, filed 30 Oct. 2013; and     -   U.S. patent application Ser. No. 14/069,286 (Attorney Docket No.         PARC-20130998US02), entitled “HASH-BASED FORWARDING OF PACKETS         WITH HIERARCHICALLY STRUCTURED VARIABLE-LENGTH IDENTIFIERS OVER         ETHERNET,” by inventors Marc E. Mosko, Ramesh C. Ayyagari, and         Subbiah Kandasamy, filed 31 Oct. 2013;     -   U.S. patent application Ser. No. 14/202,553 (Attorney Docket No.         PARC-20130888US01), entitled “SYSTEM AND METHOD FOR EFFICIENT         CONTENT CACHING IN A STREAMING STORAGE,” by inventor Marc E.         Mosko, filed 10 Mar. 2014;         the disclosures of which herein are incorporated by reference in         their entirety.

BACKGROUND

1. Field

The present disclosure relates generally to a content-centric network (CCN). More specifically, the present disclosure relates to a system and method for ranking content objects based on popularity levels.

2. Related Art

The proliferation of the Internet and e-commerce continues to fuel revolutionary changes in the network industry. Today, a significant number of information exchanges, from online movie viewing to daily news delivery, retail sales, and instant messaging, are conducted online. An increasing number of Internet applications are also becoming mobile. However, the current Internet operates on a largely location-based addressing scheme. The two most ubiquitous protocols, the Internet Protocol (IP) and Ethernet protocol, are both based on location-based addresses. That is, a consumer of content can only receive the content by explicitly requesting the content from an address (e.g., IP address or Ethernet media access control (MAC) address) closely associated with a physical object or location. This restrictive addressing scheme is becoming progressively more inadequate for meeting the ever-changing network demands.

Recently, content-centric network (CCN) architectures have been proposed in the industry. CCN brings a new approach to content transport. Instead of having network traffic viewed at the application level as end-to-end conversations over which content travels, content is requested or returned based on its unique name, and the network is responsible for routing content from the provider to the consumer. Note that content includes data that can be transported in the communication system, including any form of data such as text, images, video, and/or audio. A consumer and a provider can be a person at a computer or an automated process inside or outside the CCN. A piece of content can refer to the entire content or a respective portion of the content. For example, a newspaper article might be represented by multiple pieces of content embodied as data packets. A piece of content can also be associated with metadata describing or augmenting the piece of content with information such as authentication data, creation date, content owner, etc.

In CCN, content objects and interests are identified by their names, which is typically a hierarchically structured variable-length identifier (HSVLI). When an interest in a piece of content is received at a CCN node, a local content cache is checked to see if the content being requested exists. In addition, the CCN node may cache received content objects to increase the network response rate.

SUMMARY

One embodiment of the present invention provides a system for ranking content popularity in a content-centric network (CCN) content cache. During operation, the system receives an interest in a piece of content stored in the content cache, services the interest by accessing the piece of content, updates a service rate associated with the piece of content, updates system-wide service rate statistics, and determines a popularity level associated with the piece of content based on the updated service rate and the updated system-wide service rate statistics.

In a variation on this embodiment, updating the service rate involves calculating an exponentially weighted moving average (EWMA) of numbers of accesses to the piece of content over a time constant.

In a further variation, calculating the EWMA involves performing a table lookup to obtain pre-calculated exponential weight functions.

In a variation on this embodiment, updating the system-wide service rate statistics involves calculating a mean and a variance of a system-wide, per-object service rate.

In a further variation, the system pre-calculates a popularity threshold based on: the system-wide, per-object service rate being a random variable with a normal distribution, and the mean and the variance of the system-wide, per-object service rate.

In a further variation, determining the popularity level involves labeling the piece of content object as popular in response to the service rate associated with the piece of content equal to or greater than the pre-calculated popularity threshold.

In a variation on this embodiment, calculations associated with updating the service rate, updating the system-wide service rate statistics, and determining the popularity level are performed by a fixed-point processing unit.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 illustrates an exemplary architecture of a network, in accordance with an embodiment of the present invention.

FIG. 2 presents a diagram illustrating a pseudo-code used to compute the exponentially weighted moving average (EWMA) of a variable, in accordance with an embodiment of the present invention.

FIG. 3 presents a diagram presenting an exemplary architecture of a CCN-enabled node capable of ranking content popularity, in accordance with an embodiment of the present invention.

FIG. 4 presents a flowchart illustrating an exemplary process for determining object popularity, in accordance with an embodiment of the present invention.

FIG. 5 illustrates an exemplary system for ranking content popularity, in accordance with an embodiment.

In the figures, like reference numerals refer to the same figure elements.

DETAILED DESCRIPTION Overview

Embodiments of the present invention provide a system and method for ranking content objects based on their popularity levels. More specifically, the system relies on self-sampling paradigm and exponentially weighted moving averages to determine the top Nth percentile of popular content. During operation, when certain content is accessed, the system calculates a mean service rate of that content per sampling period. Such an object-specific mean service rate is then compared with a system-wide, per-object mean service rate. A piece of content is labeled as “popular” if its service rate is within the top Nth percentile of the system-wide per-object service rate.

In general, CCN uses two types of messages: Interests and Content Objects. An Interest carries the hierarchically structured variable-length identifier (HSVLI), also called the “name,” of a Content Object and serves as a request for that object. If a network element (e.g., router) receives multiple interests for the same name, it may aggregate those interests. A network element along the path of the Interest with a matching Content Object may cache and return that object, satisfying the Interest. The Content Object follows the reverse path of the Interest to the origin(s) of the Interest. A Content Object contains, among other information, the same HSVLI, the object's payload, and cryptographic information used to bind the HSVLI to the payload.

The terms used in the present disclosure are generally defined as follows (but their interpretation is not limited to such):

-   -   “HSVLI:” Hierarchically structured variable-length identifier,         also called a Name. It is an ordered list of Name Components,         which may be variable length octet strings. In human-readable         form, it can be represented in a format such as ccnx:/path/part.         There is not a host or query string. As mentioned above, HSVLIs         refer to content, and it is desirable that they be able to         represent organizational structures for content and be at least         partially meaningful to humans. An individual component of an         HSVLI may have an arbitrary length. Furthermore, HSVLIs can have         explicitly delimited components, can include any sequence of         bytes, and are not limited to human-readable characters. A         longest-prefix-match lookup is important in forwarding packets         with HSVLIs. For example, an HSVLI indicating an interest in         “/parc/home/bob” will match both “/parc/home/bob/test.txt” and         “/parc/home/bob/bar.txt.” The longest match, in terms of the         number of name components, is considered the best because it is         the most specific.     -   “Interest:” A request for a Content Object. The Interest         specifies an HSVLI name prefix and other optional selectors that         can be used to choose among multiple objects with the same name         prefix. Any Content Object whose name matches the Interest name         prefix and selectors satisfies the Interest.     -   “Content Object:” A data object sent in response to an Interest.         It has an HSVLI name and a Contents payload that are bound         together via a cryptographic signature. Optionally, all Content         Objects have an implicit terminal name component made up of the         SHA-256 digest of the Content Object. In one embodiment, the         implicit digest is not transferred on the wire, but is computed         at each hop, if needed.     -   “Similarity Hash:” In an Interest, the Name and several fields         called Selectors limit the possible content objects that match         the interest. Taken together, they uniquely identify the query         in the Interest. The Similarity Hash is a hash over those         fields. Two interests with the same SH are considered identical         queries.     -   “Forwarding Hash:” The forwarding hash (FH) represents the         longest matching prefix in the routing tables in various         forwarding devices (e.g., routers, switches, etc.) along a data         path that matches the Interest name. FH is computed based on one         or more components of an Interest packet's name. In general, the         source node of an Interest packet may compute FH based on the         highest-level hierarchy of the name components (wherein the         highest hierarchy is “/”).

As mentioned before, an HSVLI indicates a piece of content, is hierarchically structured, and includes contiguous components ordered from a most general level to a most specific level. The length of a respective HSVLI is not fixed. In content-centric networks, unlike a conventional IP network, a packet may be identified by an HSVLI. For example, “abcd/bob/papers/ccn/news” could be the name of the content and identifies the corresponding packet(s), i.e., the “news” article from the “ccn” collection of papers for a user named “Bob” at the organization named “ABCD.” To request a piece of content, a node expresses (e.g., broadcasts) an interest in that content by the content's name. An interest in a piece of content can be a query for the content according to the content's name or identifier. The content, if available in the network, is routed back to it from any node that stores the content. The routing infrastructure intelligently propagates the interest to the prospective nodes that are likely to have the information and then carries available content back along the path which the interest traversed.

FIG. 1 illustrates an exemplary architecture of a network, in accordance with an embodiment of the present invention. In this example, a network 180 comprises nodes 100-145. Each node in the network is coupled to one or more other nodes. Network connection 185 is an example of such a connection. The network connection is shown as a solid line, but each line could also represent sub-networks or super-networks, which can couple one node to another node. Network 180 can be content-centric, a local network, a super-network, or a sub-network. Each of these networks can be interconnected so that a node in one network can reach a node in other networks. The network connection can be broadband, wireless, telephonic, satellite, or any type of network connection. A node can be a computer system, an end-point representing users, and/or a device that can generate interest or originate content.

In accordance with an embodiment of the present invention, a consumer can generate an Interest in a piece of content and then send that Interest to a node in network 180. The piece of content can be stored at a node in network 180 by a publisher or content provider, who can be located inside or outside the network. For example, in FIG. 1, the Interest in a piece of content originates at node 105. If the content is not available at the node, the Interest flows to one or more nodes coupled to the first node. For example, in FIG. 1, the Interest flows (interest flow 150) to node 115, which does not have the content available. Next, the Interest flows (interest flow 155) from node 115 to node 125, which again does not have the content. The Interest then flows (interest flow 160) to node 130, which does have the content available. The flow of the content then retraces its path in reverse (content flows 165, 170, and 175) until it reaches node 105, where the content is delivered. Other processes such as authentication can be involved in the flow of content.

In network 180, any number of intermediate nodes (nodes 100-145) in the path between a content holder (node 130) and the Interest generation node (node 105) can participate in caching local copies of the content as it travels across the network. Caching reduces the network load for a second subscriber located in proximity to other subscribers by implicitly sharing access to the locally cached content.

Content Popularity Ranking

As described previously, in CCN, it is desirable to have intermediate nodes caching local copies of the content. This requires the intermediate nodes to have a large storage capacity because the amount of content flow through the network can be huge. In addition, the speed of the content data flow can be high, as a fast CCN router is able to process tens of millions of content packets per second. For example, a 100 Gbps (Giga bit per second) line card can process over 4 million objects per second (assuming that the size of the Interest and Content Object is 1500 bytes each). Now considering the fact that certain popular content may have a useful life of minutes or hours, and the router may need to process hundreds of millions of objects within such longer time intervals. This makes it impractical to retain all these objects in the high-speed storage (which can be a high speed RAM, a streaming storage, a directly attached storage, or a network attached storage).

In some content-caching approaches, content data are cached into a streaming storage, which allows content data to be cached as they are received. Moreover, in the streaming-storage-based content caching, as new Content Objects arrive, old Content Objects may be evicted from the cache due to the advancement of a tail pointer. Instead of treating all content equally, it is often desirable to have a differential object-replacement policy that allows popular objects to remain in the cache while allowing unpopular objects to become eligible for eviction. This can be done by placing popular content in special sectors of the streaming storage to prevent them from being over-written by the tail pointer advancement. Details about content caching in a streaming storage can be found in U.S. patent application No. TBA (Attorney Docket No. PARC-20130888US01), entitled “SYSTEM AND METHOD FOR EFFICIENT CONTENT CACHING IN A STREAMING STORAGE,” by inventor Marc E. Mosko, filed XX Mar. 2014, the disclosure of which herein is incorporated by reference in their entirety.

However, it can be challenging to obtain the ranking statistics in order to sort the content pieces into popular and unpopular piles, especially at high speed. One possible approach is to count the number of requests to each content piece and total number of requests, and determine popularity accordingly. However, considering the large number of objects processed by the router at high speed, such a counting method is impractical to implement. To solve such a problem, in some embodiments, a self-sampling technique is used to determine content popularity. Self-sampling means that the popularity level of a content piece is only evaluated when it is accessed; hence, popular content objects actually “select” themselves as they are being accessed more frequently, thus being sampled more frequently. One the other hand, unpopular content objects will be sampled much less frequently.

In some embodiments, the system calculates an exponentially weighted moving average (EWMA), which is a type of infinite impulse response (IIR) filter that applies exponentially decreased weighting factors, of the request rate (or the number of service responses per time constant τ):

Y _(i) =α*X _(i)+(1−α)*Y _(i-1),  (1)

where X_(i) is a sample being added to the moving average with weight α. Note that Eq. (1) is executed for each service request, meaning that the value of X_(i) is always “1,” and that X_(i) is added each time a request is serviced. The weight coefficient α can be derived from the filter time constant (τ) and the time lapse since the last sample (T) as:

α=1−e ^(−T/τ).  (2)

As one can see from Eq. (2) that a larger time interval between samples (the object is requested again after a relative longer time period) can result in a higher weight; on the other hand, samples occurs immediately one after another carry less weight.

Due to the finite clock resolution (which can be millisecond or tens of millisecond), multiple samples may occur at the same time step. If multiple samples are added to the filter at the same time step (same T), then the subsequent samples contribute to the EWMA as:

Y _(i) =α*X _(i) +Y _(i),  (3)

In addition to calculating the EWMA, the system can also calculate an exponentially weighted moving variance (EWMV). The calculation of the EWMV is similar to that of the EWMA, except that the squared sample error from the mean is added for each sample. In some embodiments, the EWMV is calculated as:

V _(i)=α*(X _(i) −Y _(i-1))²+(1−α)*V _(i-1).  (4)

To calculate ranking statistics, the system maintains both object-specific response rates and a system-wide total response rate, both are calculated as EWMAs. The EWMA of the number of service responses per time constant τ for a particular Content Object, defined as the object-specific response rate, can be calculated using Eqs. (1) and (3) by adding a “1” value as X_(i) each time this Content Object satisfies a request. This value can be denoted as Y_(oid), where “oid” is the object ID. In some embodiments, the oid can be derived from the HSVLI of the Content Object; or the oid can be derived from the similarity hash and/or the forwarding hash included in the header of the Content Object. More specifically, the object-specific service response rate can be calculated as:

Y _(oid,i) =α*X _(oid,i)+(1−α)*Y _(oid,i-1),  (5)

where X_(oid,i)=1 is added each time step i the Content Object “oid” satisfies a request. A similar equation to Eq. (3) is used when there are multiple updates per time interval i.

The system-wide total service response rate can be calculated as the EWMA of the number of service responses per time constant τ for all Content Objects in the system. The system-wide total response rate is denoted as Y_(sys), and can be calculated as:

Y _(sys,i) =α*X _(sys,i)+(1−α)*Y _(sys,i-1),  (6)

where X_(sys,i)=1 is added each time a service request is satisfied (by any object) in the entire system. A similar equation to Eq. (3) is used if there are multiple updates per time interval i.

In addition, the system maintains an EWMA of the per-object (averaged over all objects in the system) service rate, which can be calculated as:

A _(sys,i) =α*Y _(oid,i)+(1−α)*A _(sys,i-1).  (7)

Note that A_(sys) provides the mean of the per-object service rate, averaged over all objects in the system. Each time an individual object's service rate updates (Y_(oid) updates), the EWMA of the system-wide per-object service rate is updated with the object's service rate. The normalized system-wide, per-object response rate can then be calculated as A_(sys)/Y_(sys). In some embodiments, to avoid the division, the scaled object-specific response rate can also be calculated as Y_(oid)*Y_(sys). Note that, getting rid of the division makes it possible to use a fix-point math system (such as low-cost processors) to perform all calculations. The ranking statistics also includes the EWMV of the system-wide, per-object service rate, which can be calculated as:

V _(sys,i)=α*(Y _(oid,i) *Y _(sys,i) −A _(sys,i-1))²+(1−α)*V _(sys,i-1).  (8)

In Eq. (8), we have replaced Y_(oid,i)−A_(sys,i)/Y_(sys,i) in the first term by Y_(oid,i)*Y_(sys,i)−A_(sys,i) to avoid division. Note that given the system-wide, per-object service rate mean and the system-wide per-object service rate variance, the distribution of the system-wide, per-object service rate can be approximate as a normal distribution.

The computation of the ranking statistics, including V_(sys), A_(sys), Y_(sys), and Y_(oid), is done for each object access. Because the value of X_(i) is always “1” for Y_(oid), this computation process only requires 6 multiplications and 5 additions per sample added at the same T. Note that the first sample takes an additional multiplication and addition. To simplify the computation and avoid the need of calculation an exponential, the weight function a can be pre-calculated and stored in a lookup table for different Ts.

In order to find the top-Nth percentile popular content, one need to know the inverse error function of N, which provides the distance from a (0, 1)-Normal mean to a point that covers the top Nth percentile. In some embodiments, such inverse error functions can also be pre-calculated, often by a general-purpose central processing unit (CPU), and are stored in the network processor which performs the popularity ranking. The CPU can update the inverse error functions whenever N changes. In this disclosure, an upper threshold (t_(u), which can be a ratio) and a lower threshold and (t_(l), which can also be a ratio) are used to provide hysteresis. Depending on the level of inclusion of the inverse error function, the values of t_(u) or t_(l) may be negative

In some embodiments, the system may label a piece of content popular if, based on the computation of the ranking statistics:

$\begin{matrix} {{Y_{oid} \geq {\frac{A_{sys}}{Y_{sys}} + {t_{u}*V_{sys}}}};} & (9) \end{matrix}$

or equivalently,

Y _(oid) *Y _(sys) ≧A _(sys) +t _(u) *V _(sys) *Y _(sys),  (10)

meaning that the scalded service rate of the object oid is greater than the mean of the system-wide, per-object service rate plus a certain error value (which is t_(u) times the scaled variance). On the other hand, the system may label a piece of content unpopular if:

$\begin{matrix} {{Y_{oid} < {\frac{A_{sys}}{Y_{sys}} + {t_{l}*V_{sys}}}},} & (11) \end{matrix}$

or equivalently,

Y _(oid) *Y _(sys) <A _(sys) +t _(l) *V _(sys) *Y _(sys).  (12)

As discussed previously, the computation of the ranking statistics (V_(sys), A_(sys), Y_(sys), and Y_(oid)) only involves a small number of multiplication and addition operations for each content access event. Therefore, a piece of content's popularity rank (at least whether its access rate belongs to the top or the bottom Nth percentile among all objects in the system) can be computed using a small number of multiplications and additions per content access. In some embodiments, a network processor with a limited 32-bit arithmetic logic unit (ALU) can be used to perform the popularity ranking of Content Objects. Note that, to do so, the exponential calculation (to determine the weight functions) needs to be performed by an attached general-purpose CPU, and the result stored in a table for lookup. In further embodiments, system could cumulate the number of accesses per content object in a period, then batch process those in the next period to reduce the number of math operations at the expense of the extra bookkeeping.

FIG. 2 presents a diagram illustrating a pseudo-code used to compute the exponentially weighted moving average (EWMA) of a variable, in accordance with an embodiment of the present invention. From FIG. 2, one can see that during the initialization, if the added value (v) is the first value, only v/8 is used to start a slow ramp up to v, rather than jumping immediately to the value. Other proportionality constants could also be used, though for performance using powers of two for right shifts is efficient. The pseudo-code shown in FIG. 2 can also be used to compute EWMV according to Eq. (4), except that v is now (X−Y)², where Y is the current EWMA.

FIG. 3 presents a diagram presenting an exemplary architecture of a CCN-enabled node capable of ranking content popularity, in accordance with an embodiment of the present invention. In FIG. 3, CCN-enabled node 300 includes a packet-processing module 302, a disk-access module 304, a EWMA filter module 306, a weight table 308, and a popularity evaluator 310. Packet-processing module 302 is responsible for processing the received packets, either Interests or Content Objects, and matching the received Interest with a Content Object in the storage. Once a match is found, disk-access module 304 accesses the storage to retrieve the corresponding Content Object. Each content-access event is sent to EWMA filter module 306, which is responsible for updating the various ranking statistics, which can include V_(sys), A_(sys), Y_(sys), and Y_(oid). Note that Y_(oid) for a particular object ID only updates when a request to an object with the particular object ID is serviced, whereas the other three variables will be updated each time when any object in the storage is accessed. In some embodiments, EWMA filter module 306 is implemented using a network processor with limited 32-bit ALU, which has a limited computation power. To ensure high speed, the exponential weight functions needed for the EWMA calculation are obtained by performing a table lookup in weight table 308. In some embodiments, weight table 308 is calculated beforehand by a general-purpose CPU, which can be part of the switch or router CPU. In some embodiments, weight table 308 can include a set of weight functions calculated using a filter time constant (for example, 10 seconds or 1 minute) and different sampling times. A system administrator can define the filter time constant. Depending on the clock resolution, the weight table can have different numbers of entries. For example, if the clock resolution is 1 millisecond, then a table of 2048 entries covers over 2 seconds, while up to 10,000 entries would be needed to cover the time window provided by the filter constant (10 seconds).

The calculated ranking statistics are then sent to popularity evaluator 310, which determines whether a Content Object is popular or unpopular. The outcomes of the popularity evaluation are then sent back to disk-access module 304. Popularity evaluator 310 may determine a particular Content Object is popular if the service rate of the Content Object is within the top Nth percentile (such as the top 20%) of the system-wide, per-object service rate. In some embodiments, popularity evaluate 310 determines whether the Content Object is popular based on formula (9) or (10), where the upper threshold t_(u) is pre-calculated based on N. In some embodiments, once determined popular, the Content Object may be moved to a special storage sector to prevent it from being over-written. On the other hand, popularity evaluator 310 may determine a particular Content Object is unpopular if the service rate of the Content Object is within the bottom Mth percentile (such as the bottom 20%) of the system-wide, per-object service rate. In some embodiments, popularity evaluate 310 determines whether the Content Object is unpopular based on formula (11) or (12), where the lower threshold t_(l) is pre-calculated based on the desired M. In some embodiments, once determined unpopular, a Content Object may be ready for immediate eviction.

In some embodiments, instead of labeling objects within the top Nth percentile of the service rate as popular, popularity evaluator 310 may label the top K objects (with the highest service rate) as popular and the bottom L objects (with the lowest service rate) as unpopular, thus keeping the number of objects in the popular sector of the storage roughly constant. To do so, the system can implement a proportional-integral-derivative controller (PID-controller) to calculate the error values, hence the service rate upper threshold t_(u) and lower threshold t_(l), based on the desired number of popular objects.

As time progresses, Content Objects that are marked as popular (base on their recent service rate being above the point defined by t_(u)) may become unpopular. When such events occur (the original popular objects' service rates drop below the point defined by t_(u)), these objects may be moved out of the popular sectors of the storage, making them candidates for future evictions. However, in certain scenarios, a popular object may become unpopular suddenly such that its service rate falls to identically zero, meaning that there will be no further sampling, and thus updating of the service rate, of that object. As a result, this particular object may continue to be labeled as “popular,” occupying the premium storage space even though no Interest for such an object arrives. To avoid such a situation, in some embodiments, the system implements a background task to slowly search for such instant deaths.

FIG. 4 presents a flowchart illustrating an exemplary process for determining object popularity, in accordance with an embodiment of the present invention. During operation, the system services an Interest with a Content Object obtained from a storage (operation 402). In some embodiments, the storage may be an attached storage or a streaming storage. The system updates the service request rate associated with this particular Content Object (operation 404), and the total service rate associated with the entire system (operation 406). Note that only the service rate associated with this particular Content Object is updated, while service rates associated with all other objects remain the same until they are being accessed. In some embodiments, the service request rates for the object and the system can be calculated as the EWMA of the number of service requests for the particular object and for the system, respectively. In further embodiments, calculating the EWMAs may involve performing a table lookup to obtain the exponential weights.

In addition, the system updates the system-wide, per-object service rate statistics, including the mean and the variance (averaged over all objects) (operation 408). The system then determines at what percentile an object can be labeled as popular (operation 410), and obtains the error values using an inverse error function of that percentile (operation 412). For example, the system may determine that if an object has a service rate that is higher than 80% of the system-wide, per-object service rates, it can be labeled as popular; or if the object has a service rate that is lower than 80% of the system-wide per-object service rates, it can be labeled as unpopular. In some embodiments, all objects that are not popular are labeled as unpopular. The higher the percentile, the fewer the objects would be labeled as popular. In some embodiments, the inverse error functions are obtained by looking up a pre-calculated table. The percentage point can be defined by an administrator, or be determined based on available storage. In further embodiments, instead of using the percentile value to determine error values, the system may calculate the error values based on a fixed number of popular items.

Subsequently, the system evaluates the object's popularity level, which can be a ternary value (“1” for popular, 0 for neutral, and “−1” for unpopular) (operation 414), or a binary value (“1” for popular and “0” for unpopular). In some embodiments, the popularity determination is based on the object-specific service rate, the system-wide total service rate (as a normalization factor), the system-wide, per-object service rate mean, the system-wide, per-object service rate variance, and the computed error values (or distance to the mean value), as shown by formulas (9)-(12).

Computer and Communication System

FIG. 5 illustrates an exemplary system for ranking content popularity, in accordance with an embodiment. A system 500 for ranking content popularity comprises a processor 510, a memory 520, and a storage 530. Storage 530 typically stores instructions which can be loaded into memory 520 and executed by processor 510 to perform the methods mentioned above. In one embodiment, the instructions in storage 530 can implement a packet-processing module 532, a disk-access module 534, an EWMA filter module 536, and a popularity evaluation module 538, all of which can be in communication with each other through various means.

In some embodiments, modules 532, 534, 536, and 538 can be partially or entirely implemented in hardware and can be part of processor 510. Further, in some embodiments, the system may not include a separate processor and memory. Instead, in addition to performing their specific tasks, modules 532, 534, 536, and 538, either separately or in concert, may be part of general- or special-purpose computation engines.

Storage 530 stores programs to be executed by processor 510. Specifically, storage 530 stores a program that implements a system (application) for ranking popularity for content stored in a steaming storage, such as streaming storage 540. During operation, the application program can be loaded from storage 530 into memory 520 and executed by processor 510. As a result, system 500 can perform the functions described above. System 500 can be coupled to an optional display 580, keyboard 560, and pointing device 570, and also be coupled via one or more network interfaces to network 582.

The data structures and code described in this detailed description are typically stored on a computer-readable storage medium, which may be any device or medium that can store code and/or data for use by a computer system. The computer-readable storage medium includes, but is not limited to, volatile memory, non-volatile memory, magnetic and optical storage devices such as disk drives, magnetic tape, CDs (compact discs), DVDs (digital versatile discs or digital video discs), or other media capable of storing computer-readable media now known or later developed.

The methods and processes described in the detailed description section can be embodied as code and/or data, which can be stored in a computer-readable storage medium as described above. When a computer system reads and executes the code and/or data stored on the computer-readable storage medium, the computer system performs the methods and processes embodied as data structures and code and stored within the computer-readable storage medium.

Furthermore, methods and processes described herein can be included in hardware modules or apparatus. These modules or apparatus may include, but are not limited to, an application-specific integrated circuit (ASIC) chip, a field-programmable gate array (FPGA), a dedicated or shared processor that executes a particular software module or a piece of code at a particular time, and/or other programmable-logic devices now known or later developed. When the hardware modules or apparatus are activated, they perform the methods and processes included within them.

The above description is presented to enable any person skilled in the art to make and use the embodiments, and is provided in the context of a particular application and its requirements. Various modifications to the disclosed embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the present disclosure. Thus, the present invention is not limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein. 

What is claimed is:
 1. A computer-executable method for ranking content popularity in a content-centric network (CCN) content cache, the method comprising: receiving, by a CCN node, an interest in a piece of content stored in the content cache; servicing the interest by accessing the piece of content; updating a service rate associated with the piece of content; updating system-wide service rate statistics; and determining a popularity level associated with the piece of content based on the updated service rate and the updated system-wide service rate statistics.
 2. The method of claim 1, wherein updating the service rate involves calculating an exponentially weighted moving average (EWMA) of numbers of accesses to the piece of content over a time constant.
 3. The method of claim 2, wherein calculating the EWMA involves performing a table lookup to obtain pre-calculated exponential weight functions.
 4. The method of claim 1, wherein updating the system-wide service rate statistics involves calculating a mean and a variance of a system-wide, per-object service rate.
 5. The method of claim 4, further comprising pre-calculating a popularity threshold based on: the system-wide, per-object service rate being a random variable with a normal distribution; and the mean and the variance of the system-wide, per-object service rate.
 6. The method of claim 5, wherein determining the popularity level involves labeling the piece of content object as popular in response to the service rate associated with the piece of content equal to or greater than the pre-calculated popularity threshold.
 7. The method of claim 1, wherein calculations associated with updating the service rate, updating the system-wide service rate statistics, and determining the popularity level are performed by a fixed-point processing unit.
 8. An system for ranking content popularity in a content-centric network (CCN) content cache, the system comprising: a processor; and a storage device coupled to the processor and storing instructions which when executed by the processor cause the processor to perform a method, the method comprising: receiving, by a CCN node, an interest in a piece of content stored in the content cache; servicing the interest by accessing the piece of content; updating a service rate associated with the piece of content; updating system-wide service rate statistics; and determining a popularity level associated with the piece of content based on the updated service rate and the updated system-wide service rate statistics.
 9. The system of claim 8, wherein updating the service rate involves calculating an exponentially weighted moving average (EWMA) of numbers of accesses to the piece of content over a time constant.
 10. The system of claim 9, wherein calculating the EWMA involves performing a table lookup to obtain pre-calculated exponential weight functions.
 11. The system of claim 8, wherein updating the system-wide service rate statistics involves calculating a mean and a variance of a system-wide, per-object service rate.
 12. The system of claim 11, wherein the method further comprises pre-calculating a popularity threshold based on: the system-wide, per-object service rate being a random variable with a normal distribution; and the mean and the variance of the system-wide, per-object service rate.
 13. The system of claim 12, wherein determining the popularity level involves labeling the piece of content object as popular in response to the service rate associated with the piece of content equal to or greater than the pre-calculated popularity threshold.
 14. The system of claim 8, wherein calculations associated with updating the service rate, updating the system-wide service rate statistics, and determining the popularity level are performed by a fixed-point processing unit.
 15. A non-transitory computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for ranking content popularity in a content-centric network (CCN) content cache, the method comprising: receiving, by a CCN node, an interest in a piece of content stored in the content cache; servicing the interest by accessing the piece of content; updating a service rate associated with the piece of content; updating system-wide service rate statistics; and determining a popularity level associated with the piece of content based on the updated service rate and the updated system-wide service rate statistics.
 16. The computer-readable storage medium of claim 15, wherein updating the service rate involves calculating an exponentially weighted moving average (EWMA) of numbers of accesses to the piece of content over a time constant.
 17. The computer-readable storage medium of claim 16, wherein calculating the EWMA involves performing a table lookup to obtain pre-calculated exponential weight functions.
 18. The computer-readable storage medium of claim 15, wherein updating the system-wide service rate statistics involves calculating a mean and a variance of a system-wide, per-object service rate.
 19. The computer-readable storage medium of claim 18, wherein the method further comprises pre-calculating a popularity threshold based on: the system-wide, per-object service rate being a random variable with a normal distribution; and the mean and the variance of the system-wide, per-object service rate.
 20. The computer-readable storage medium of claim 19, wherein determining the popularity level involves labeling the piece of content object as popular in response to the service rate associated with the piece of content equal to or greater than the pre-calculated popularity threshold.
 21. The computer-readable storage medium of claim 15, wherein calculations associated with updating the service rate, updating the system-wide service rate statistics, and determining the popularity level are performed by a fixed-point processing unit. 